Method for processing received signal by forming RE group in MIMO receiver

ABSTRACT

Disclosed are a method and a receiver for processing a received signal, the method dividing a plurality of resource elements (RE) to create RE groups by considering the inter-relationships between the channels of the plurality of REs, selecting a reference RE for each RE group, and generating, for each RE group, a detection signal from a received signal on the basis of channel information of the reference RE.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is the National Stage filing under 35 U.S.C. 371 ofInternational Application No. PCT/KR2015/004396, filed on Apr. 30, 2015,which claims the benefit of U.S. Provisional Application No. 61/990,662,filed on May 8, 2014, the contents of which are all hereby incorporatedby reference herein in their entirety.

TECHNICAL FIELD

The present invention relates to technology related to a method ofreducing implementation complexity while minimizing performancedeterioration of a receiver in a massive multiple input multiple output(MIMO) environment.

BACKGROUND ART

A multiple input multiple output (MIMO) system refers to a wirelesscommunication system using multiple transmit antennas and multiplereceive antennas. In a MIMO system, fading effects occurring in a radiochannel may be minimized via a diversity scheme or a plurality ofstreams may be simultaneously transmitted via spatial multiplexing,thereby improving throughput. If the number of transmit antennas isN_(t) and the number of receive antennas is N_(r), a maximum number ofstreams transmittable in a spatial multiplexing (SM) scheme is min(N_(t), N_(r)). In particular, in a high signal-to-noise ratio (SNR)environment, it is known that the slope of communication capacity is min(N_(t), N_(r)). Since communication capacity means a maximum amount ofinformation theoretically transmittable on a given channel,communication capacity also increases when the numbers of transmit andreceive antennas simultaneously increase.

A massive MIMO system having vast transmit and receive antennas isattracting considerable attention as 5G technology. In many papers andexperiments, the massive MIMO system includes one base station(including a distributed antenna system) having multiple antennas and aplurality of user equipments (UEs) each having one antenna. In thiscase, since the UE has one antenna but several UEs simultaneouslyreceive services from one base station, channels between the basestation and the UEs may be understood as MIMO. If the total number ofUEs is K, the slope of communication capacity is expressed by min(N_(t), K) in a high SNR environment.

Theoretically, when a base station having an infinite number of transmitantennas simultaneously transmits data to several UEs, an optimaltransmission algorithm of the base station is a maximal ratiotransmission (MRT) algorithm. When one base station receives datatransmitted from several UEs to the base station, an optimal receptionalgorithm of the base station is a maximal ratio combining (MRC)algorithm. Since the MRT and MRC algorithms do not take into accountinterference, if the number of antennas is finite, performancedeterioration occurs but, if the number of antennas is infinite,interference disappears. Therefore, the MRT and MRC algorithms maybecome optimal solutions.

Since a base station can generate a sharp beam sharp via antennabeamforming, the base station may concentrate energy on a specific UE.In this case, the same information may be transmitted with low power andinterference with neighboring UEs may be minimized, thereby minimizingsystem performance deterioration.

DISCLOSURE Technical Problem

An object of the present invention devised to solve the problem lies ina method of reducing received-signal detection complexity whilemaintaining performance of a receiver in a massive multiple inputmultiple output (MIMO) environment.

Another object of the present invention is to efficiently improvecomputational complexity by adaptively selecting the size of an RE groupbased on channel correlation between REs, a signal to noise ratio (SNR)and an operational result of a receiver.

The technical problems solved by the present invention are not limitedto the above technical problems and other technical problems which arenot described herein will become apparent to those skilled in the artfrom the following description.

Technical Solution

The object of the present invention can be achieved by providing amethod of processing a received signal at a multiple input multipleoutput (MIMO) receiver including a plurality of antennas includingforming one or more resource element (RE) groups by grouping a pluralityof REs in consideration of channel correlation between the plurality ofREs, selecting a reference RE with respect to each of the one or more REgroups, and generating a detection signal from the received signal basedon channel information of the reference RE with respect to each of theone or more RE groups.

Each of the one or more RE groups may include a plurality of REsarranged along a frequency axis and a time axis and the configuration ofeach of the RE groups may be determined based on the number and shape ofthe plurality of arranged REs.

The selecting the reference RE may include selecting, as the referenceRE, an RE located at a position where a maximum distance from another REamong the plurality of REs included in each of the one or more RE groupsis minimized, and the maximum distance may be expressed by a distance onthe frequency axis and a distance on the time axis.

The forming the RE group may include comparing the channel correlationbetween the plurality of REs computed along the frequency axis with afirst threshold and comparing the channel correlation between theplurality of REs computed along the time axis with a second threshold toform the one or more RE groups.

The forming the RE group may include selecting an RE before the channelcorrelation computed along the frequency axis becomes less than thefirst threshold as a border on the frequency axis of the RE group andselecting an RE before the channel correlation computed along the timeaxis becomes less than the second threshold as a border on the time axisof the RE group.

The forming the RE group may include determining an error allowablecoefficient of a numerical analysis algorithm to be used in a process ofgenerating the detection signal based on at least one of a signal tonoise ratio (SNR), signal to interference ratio (SIR) and signal tointerference plus noise ratio (SINR) of the received signal of each ofthe plurality of REs.

The forming the RE group may include determining a threshold to be usedin a process of computing the channel correlation based on at least oneof the SNR, SIR and SINR of the received signal of each of the pluralityof REs.

The method may further include controlling the configuration of each ofthe one or more RE groups based on a convergence speed of a numericalanalysis algorithm performed in a process of generating the detectionsignal.

The controlling may include comparing increment of an iteration numberin a frequency-axis direction with increment of an iteration number in atime-axis direction and reducing the size of the RE group in an axisdirection in which the iteration number is more rapidly increased.

The forming the RE group may include forming the one or more RE groupsby dividing a mother group composed of a resource block (RB), a subframeor a slot.

In another aspect of the present invention, provided herein is amultiple input multiple output (MIMO) receiver including a plurality ofantennas and configured to process a signal received through theplurality of antennas including a transmitter, a receiver, and aprocessor connected to the transmitter and the receiver and configuredto process the received signal, wherein the processor forms one or moreresource element (RE) groups by grouping a plurality of REs inconsideration of channel correlation between the plurality of REs,selects a reference RE with respect to each of the one or more REgroups, and generates a detection signal from the received signal basedon channel information of the reference RE with respect to each of theone or more RE groups.

Advantageous Effects

According to the embodiments of the present invention have the followingeffects.

First, by grouping a plurality of REs into an RE group to generatedetection signals, it is possible to obtain computational complexitygain without performance deterioration.

Second, by adaptively determining an RE group according to acommunication environment, it is possible to reduce the operationalburden of a receiver.

The effects of the present invention are not limited to theabove-described effects and other effects which are not described hereinmay be derived by those skilled in the art from the followingdescription of the embodiments of the present invention. That is,effects which are not intended by the present invention may be derivedby those skilled in the art from the embodiments of the presentinvention.

DESCRIPTION OF DRAWINGS

The accompanying drawings, which are included to provide a furtherunderstanding of the invention, illustrate embodiments of the inventionand together with the description serve to explain the principle of theinvention. The technical features of the present invention are notlimited to specific drawings and the features shown in the drawings arecombined to construct a new embodiment. Reference numerals of thedrawings mean structural elements.

FIG. 1 is a diagram showing computational complexity according to thenumber of received streams in a multiple input multiple output (MIMO)environment, in relation to the present invention.

FIG. 2 is a diagram showing memory requirements according to the numberof received streams in a MIMO environment, in relation to the presentinvention.

FIG. 3 is a diagram showing interference between UEs in the same cell ina MIMO environment in relation to the present invention.

FIG. 4 is a diagram showing interference between neighboring cells in aMIMO environment in relation to the present invention.

FIG. 5 is a diagram showing the structure of a resource block (RB)assigned to a UE in relation to the present invention.

FIG. 6 is a diagram showing a resource element (RE) group formed by aplurality of REs in relation to the present invention.

FIG. 7 is a diagram showing a conventional MIMO receiver operationalprocess in relation to the present invention.

FIG. 8 is a diagram showing a MIMO receiver operational process relatedto the present invention.

FIG. 9 is a diagram showing the concept of a process of detecting adetection signal at a MIMO receiver related to the present invention.

FIG. 10 is a diagram showing the concept of a process of detecting adetection signal at a MIMO receiver related to the present invention.

FIG. 11 is a diagram showing an example of generating a preprocessingfilter at a MIMO receiver related to the present invention.

FIG. 12 is a diagram showing another MIMO receiver operational processrelated to the present invention.

FIG. 13 is a diagram showing another MIMO receiver operational processrelated to the present invention.

FIG. 14 is a diagram showing another MIMO receiver operational processrelated to the present invention.

FIG. 15 is a graph showing comparison between computational complexitiesof signal detection processes related to the present invention.

FIG. 16 is a diagram showing a process of forming RE groups according toan embodiment of the present invention.

FIG. 17 is a diagram showing a process of forming RE groups according toan embodiment of the present invention.

FIG. 18 is a diagram showing a process of forming RE groups according toan embodiment of the present invention.

FIG. 19 is a diagram showing a process of forming RE groups according toan embodiment of the present invention.

FIG. 20 is a diagram showing a process of forming RE groups according toan embodiment of the present invention.

FIG. 21 is a diagram showing a process of forming RE groups according toan embodiment of the present invention.

FIG. 22 is a block diagram showing the configuration of a UE and a basestation according to one embodiment of the present invention.

BEST MODE

Although the terms used in the present invention are selected fromgenerally known and used terms, terms used herein may be varieddepending on operator's intention or customs in the art, appearance ofnew technology, or the like. In addition, some of the terms mentioned inthe description of the present invention have been selected by theapplicant at his or her discretion, the detailed meanings of which aredescribed in relevant parts of the description herein. Furthermore, itis required that the present invention is understood, not simply by theactual terms used but by the meanings of each term lying within.

The following embodiments are proposed by combining constituentcomponents and characteristics of the present invention according to apredetermined format. The individual constituent components orcharacteristics should be considered optional factors on the conditionthat there is no additional remark. If required, the individualconstituent components or characteristics may not be combined with othercomponents or characteristics. In addition, some constituent componentsand/or characteristics may be combined to implement the embodiments ofthe present invention. The order of operations to be disclosed in theembodiments of the present invention may be changed. Some components orcharacteristics of any embodiment may also be included in otherembodiments, or may be replaced with those of the other embodiments asnecessary.

In describing the present invention, if it is determined that thedetailed description of a related known function or construction rendersthe scope of the present invention unnecessarily ambiguous, the detaileddescription thereof will be omitted.

In the entire specification, when a certain portion “comprises orincludes” a certain component, this indicates that the other componentsare not excluded and may be further included unless specially describedotherwise. The terms “unit”, “-or/er” and “module” described in thespecification indicate a unit for processing at least one function oroperation, which may be implemented by hardware, software or acombination thereof. The words “a or an”, “one”, “the” and words relatedthereto may be used to include both a singular expression and a pluralexpression unless the context describing the present invention(particularly, the context of the following claims) clearly indicatesotherwise.

In this document, the embodiments of the present invention have beendescribed centering on a data transmission and reception relationshipbetween a mobile station and a base station. The base station may mean aterminal node of a network which directly performs communication with amobile station. In this document, a specific operation described asperformed by the base station may be performed by an upper node of thebase station.

Namely, it is apparent that, in a network comprised of a plurality ofnetwork nodes including a base station, various operations performed forcommunication with a mobile station may be performed by the basestation, or network nodes other than the base station. The term basestation may be replaced with the terms fixed station, Node B, eNode B(eNB), advanced base station (ABS), access point, etc.

The term mobile station (MS) may be replaced with user equipment (UE),subscriber station (SS), mobile subscriber station (MSS), mobileterminal, advanced mobile station (AMS), terminal, etc.

A transmitter refers to a fixed and/or mobile node for transmitting adata or voice service and a receiver refers to a fixed and/or mobilenode for receiving a data or voice service. Accordingly, in uplink, amobile station becomes a transmitter and a base station becomes areceiver. Similarly, in downlink transmission, a mobile station becomesa receiver and a base station becomes a transmitter.

Communication of a device with a “cell” may mean that the devicetransmit and receive a signal to and from a base station of the cell.That is, although a device substantially transmits and receives a signalto a specific base station, for convenience of description, anexpression “transmission and reception of a signal to and from a cellformed by the specific base station” may be used. Similarly, the term“macro cell” and/or “small cell” may mean not only specific coverage butalso a “macro base station supporting the macro cell” and/or a “smallcell base station supporting the small cell”.

The embodiments of the present invention can be supported by thestandard documents disclosed in any one of wireless access systems, suchas an IEEE 802.xx system, a 3^(rd) Generation Partnership Project (3GPP)system, a 3GPP Long Term Evolution (LTE) system, and a 3GPP2 system.That is, the steps or portions, which are not described in order to makethe technical spirit of the present invention clear, may be supported bythe above documents.

In addition, all the terms disclosed in the present document may bedescribed by the above standard documents. In particular, theembodiments of the present invention may be supported by at least one ofP802.16-2004, P802.16e-2005, P802.16.1, P802.16p and P802.16.1bdocuments, which are the standard documents of the IEEE 802.16 system.

Hereinafter, the preferred embodiments of the present invention will bedescribed with reference to the accompanying drawings. It is to beunderstood that the detailed description which will be disclosed alongwith the accompanying drawings is intended to describe the exemplaryembodiments of the present invention, and is not intended to describe aunique embodiment which the present invention can be carried out.

It should be noted that specific terms disclosed in the presentinvention are proposed for convenience of description and betterunderstanding of the present invention, and the use of these specificterms may be changed to another format within the technical scope orspirit of the present invention.

1. Massive MIMO Receiver

To establish a massive MIMO system, a massive MIMO reception algorithmmust be developed. As compared to an existing MIMO system, in a massiveMIMO system, a receiver needs to be improved in terms of the followingtwo aspects.

First, in a massive MIMO environment, the number of data streamssimultaneously received by the receiver increases. Increase in thenumber of simultaneously processed data streams leads to increase incomputational complexity and memory requirements. This leads to increasein system implementation cost and processing time, thereby imposing aburden on a reception system. Computational complexity and memoryrequirements according to the number of received streams of an existingMIMO reception algorithm exponentially increase as shown in FIGS. 1 and2.

Second, in the massive MIMO environment, as the number of interferencesources increases, a reception algorithm having improved interferencecancellation performance is required. In the massive MIMO system, when abase station simultaneously transmits data to several tens or hundredsof users, each user receives several tens or more of multi-userinterference signals except for a data signal transmitted thereto.Accordingly, there is a need for a massive MIMO reception algorithm forefficiently cancelling such interference signals. In addition, efficientcancellation of interference received from neighboring cells or users ofneighboring cells is also required.

In order to solve such technical problems, the following technicalissues are considered.

First, increase in computational complexity and memory requirements in amassive MIMO environment will be described. If the number of antennas ofa transmitter is always greater than the number of antennas of areceiver, the number of streams transmitted by the transmitter isincreased in proportion to the number of antennas of the receiver. Atthis time, the receiver uses a reception filter in order to detect eachstream from a received signal. In an LTE system, the filter should berecomputed in every subframe.

Load caused due to such a computation process may be quantified tocomputational complexity and memory requirements. Complexity and memoryrequirements are proportional to the square or cube of the number ofreceived streams. Accordingly, as the number N_(s) of received streamsincreases, computational complexity and memory requirements rapidlyincrease, as shown in FIG. 1. Further, since hardware specification isdetermined by the worst case, hardware implementation cost significantlyincreases as the number of streams increases.

Hereinafter, a reception algorithm of a conventional MIMO receiverand/or computational complexity and memory requirements according tofilter will be described.

The MRC algorithm requires smallest computational complexity O(N_(s) ²)and memory. However, the MRC algorithm does not take into accountinterference between streams and thus provides low performance (that is,low reception SINR).

A minimum mean square error (MMSE) filter provides the best performance(that is, high reception SINR) among linear detection methods. However,complexity is expressed by O(N_(s) ³) and O(N_(s) ²) additional memoriesfor inverse matrix operation are required. FIGS. 1 and 2 show complexityand memory requirements according to the number of received streams ofthe MMSE filter, respectively.

For reception using the MMSE filter, an inverse matrix operation for achannel matrix is necessary. The size of the inverse matrix isdetermined by the number of received streams and, for example, a timerequired for a high performance field programmable gate array (FPGA) toobtain a 15×15 inverse matrix is about 150 μs. Such time delaycorresponds to about 30% of a coherence time of 500 μs assumed in an LTEchannel model.

In addition, for inverse matrix operation for MMSE reception, a processof moving all channel information to a new memory is necessary, therebyleading to significant delay. In addition, a processor accesses a memoryfor inverse matrix operation, thereby leading to additional delay. Suchdelay significantly increases system processing time.

Lastly, an interference cancellation (IC) filter is a non-lineardetection method and can obtain performance corresponding to maximumcommunication capacity in a D-BLAST receiver which is an example of IC.A V-BLAST receiver having low implementation complexity is configured inthe form of a hybrid of MMSE and SIC. In particular, in a MIMO-OFDMenvironment, the V-BLAST receiver has performance close to maximumcommunication capacity as channel selectivity increases. However, sincethe V-BLAST receiver is also based on the MMSE filter, complexity andmemory requirements higher than those of the MMSE filter are required.

In addition, the IC method cancels previously detected symbols andlayers from a received signal to control interference. Accordingly, ifthe previously detected values have errors, an error propagationphenomenon in which detection performance deteriorates occurs. VariousIC algorithms for solving such a problem have been proposed but haveincreased complexity as compared to the conventional method.

FIG. 3 is a diagram showing interference between UEs in the same cell ina MIMO environment in relation to the present invention. FIG. 4 is adiagram showing interference between neighboring cells in a MIMOenvironment in relation to the present invention. In addition toincrease in computational complexity and memory requirements,interference occurring in a massive MIMO environment will be describedwith reference to FIGS. 3 and 4.

If the number of antennas of a base station is large, one base stationmay simultaneously support a plurality of UEs. In this case, a signaltransmitted from the base station to a UE A acts as interference withrespect to a UE B and a signal transmitted to the UE B acts asinterference with respect to the UE A. Since the interference istransmitted by the base station along with a desired signal, theinterference undergoes the same path loss as the desired signal.Accordingly, power of the interference signal is similar to that of thedesired signal and thus a reception SINR is rapidly reduced. In order tosolve such a problem, the base station may perform multi user (MU)-MIMOprecoding to minimize interference. However, even in this case, it isdifficult to completely cancel multi-user interference due to channelinformation errors, aging phenomena and codebook size restriction.

In a multi-cell environment, interference among various cells is caused.Representatively, in the environment of FIG. 4, the UE A is influencedby interference from a base station B and the UE B is influenced byinterference from a base station A. In particular, when a UE is close toa boundary between neighboring cells, the UE receives strongerinterference from the neighboring base station. In addition, when a gapbetween cells is narrow as in a small cell (e.g., a micro cell, a picocell, a femto cell, etc.), a probability that a UE receives stronginterference from a neighboring cell is further increased.

In a dense multi-cell environment employing a massive MIMO method,interference cancellation capabilities of a MIMO receiver need to beimproved. In particular, if strong interference is caused, aninterference cancellation (IC) reception algorithm is required and anexisting IC receiver requires more antennas than the number ofinterference sources. For example, the receiver requires 11 receiveantennas in order to cancel 10 interference sources. In a small-sized UEin which a sufficient number of antennas may not be mounted,technologies for solving such a limitation need to be introduced. Forexample, improved interference suppression (IS) technology applies tomulti-user or multi-cell interference or interference alignmenttechnology is utilized in a transmitter to align interference in aspecific signal space and an IC receiver is applied to cancelinterference from many interference sources using a restricted number ofreceive antennas.

Subsequently, an operation algorithm of a conventional MIMO receiverwill be described in relation to the above-described problems. FIG. 5 isa diagram showing the structure of a resource block (RB) assigned to aUE in relation to the present invention. FIG. 6 is a diagram showing aresource element (RE) group formed by a plurality of REs in relation tothe present invention. FIG. 7 is a diagram showing a conventional MIMOreceiver operational process in relation to the present invention.

FIG. 5 shows one RB assigned to a specific UE and vertical andhorizontal axes respectively denote frequency and time axes. One RB iscomposed of N_(SC) ^(RB)N_(symb) ^(DL) REs and, in each RE, a receivedsignal is expressed by Equation 1 below.y _(l) =G _(l) s _(l) +i _(l) +w _(l) , l=0, . . . , N _(SC) ^(RB) N_(sym) ^(DL)−1  Equation 1

In Equation 1, l denotes an index of an RE, G_(l) denotes a channelestimated via a demodulation reference signal (DMRS) in a receiver,S_(l) denotes a transmitted signal, and I_(l) denotes interference.w_(l) denotes white noise and a covariance matrix of w_(l) is σ_(w) ²I.

As described above, the receiver may use a minimum mean square error(MMSE) filter in order to cancel influence of a channel from a receivedsignal. A transmitted signal detected from the received signal using theMMSE filter is expressed by Equation 2 below.ŝ _(l) =B _(l) y _(l) with B _(l)=(G _(l) ^(H) G _(l) +R _(l))⁻¹ G _(l)^(H)  Equation 2

In Equation 2, B_(l) denotes an MMSE filter and ŝ_(l) denotes atransmitted signal estimated via the MMSE filter. A covariance matrixR_(l) is defined as R_(l) =i _(l) i _(l) ^(H)+σ_(w) ²I. At this time,computational complexity of multiplication of complex numbers necessaryto estimate the transmitted signal using the MMSE filter may beschematically expressed by Equation 3 below.

$\begin{matrix}{\left( {{\frac{1}{2}N_{r}N_{s}^{2}} + {\frac{1}{2}N_{s}^{3}} + N_{s}^{2} + {N_{r}N_{s}}} \right)N_{RB}^{DL}N_{symb}^{DL}} & {{Equation}\mspace{14mu} 3}\end{matrix}$

In case of massive MIMO, the number N_(r) of receive antennas is largeand, in this case, streams corresponding in number N_(s) to a maximumnumber of receive antennas may be received. In this case, communicationcapacity of the receiver may be improved by a maximum of N_(s) times butcomplexity is rapidly increased in proportion to the cube O(N_(s) ³) ofthe number of streams. Accordingly, if the number of received streams islarge, a receiver capable of performing processing with low complexitywhile minimizing performance deterioration is necessary.

FIG. 6 shows a portion of an RB of FIG. 5 and shows an RE group composedof several REs. At this time, channels of the REs may have mutualcorrelation. In particular, if the Doppler effect is small (the receiveris slowly moved or is fixed), correlation between the REs located on thesame horizontal axis is large. If power delay spread of a channel islow, correlation between the REs located on the same vertical axis islarge. If the Doppler effect is small and power delay spread of thechannel is low, correlation between all REs shown in FIG. 6 is large. InFIG. 6, correlation between a center RE and a peripheral RE is shown bythe depth of shade. That is, as the depth of shade of each RE increases,correlation with the center RE increases and, as the depth of shade ofeach RE decreases, correlation with the center RE decreases.

As shown in FIG. 7, a conventional MIMO receiver has applied to the sameoperation to REs without considering correlation between the REs todetect a transmitted signal. That is, the conventional MIMO receiver hasperformed a process of computing a filter B_(i) from channel informationG_(i) per RE with respect to a received signal (710) and detecting anddecoding a received signal with respect to each RE (720). However, whentaking into account increase in computational complexity and memoryrequirements due to increase in number of streams in a massive MIMOenvironment, a conventional reception algorithm needs to be improved.

Hereinafter, a MIMO receiver operating according to an algorithm havinglower complexity while providing the same performance as an existingalgorithm using correlation between REs is proposed.

2. Method of Operating MIMO Receiver Using Preprocessing Filter

FIG. 8 is a diagram showing a process of operating a MIMO receiver usinga preprocessing filter according to an embodiment of the presentinvention.

A MIMO receiver using the preprocessing filter configures a plurality ofREs having relatively large correlation between channels as one RE group(having a size of N), as described with reference to FIG. 6.Hereinafter, a signal Ŝ_(l) detected using a received signal detector(e.g., an MMSE filter) from a received signal of an l-th RE of an REgroup is defined as a “detection signal”. In the case of the MIMOreceiver described with reference to FIG. 7, if the number of layers islarge in a process of detecting the detection signal from the receivedsignal, the complexity problems of FIG. 1 occur. In order to reduce suchcomplexity, the proposed MIMO receiver uses a numerical analysisalgorithm (e.g., a conjugate gradient (CG) algorithm), instead ofdirectly computing the MMSE filter to detect the detection signals ofthe REs of the RE group.

Hereinafter, v₁ means a “preprocessing filter (or an accelerationfilter)” generated based on the MIMO channel of a first RE of the REgroup. The above-described numerical analysis algorithm finds a valuethrough an iterative computation process and a value becomes close to anaccurate value as the iterative computation process proceeds. If thepreprocessing filter v₁ is used in the iterative computation process,the MIMO receiver can find a desired value with a small iteration number(that is, at a high speed).

However, generating the preprocessing filter capable of sufficientlyincreasing the speed in order to find the desired value as describedabove requires high complexity. Accordingly, in order to decreasecomputational complexity of the case of obtaining the respectivepreprocessing filters with respect to all of the REs of the RE group, apreprocessing filter may be generated with respect to a specific RE(e.g., the first RE) and may be shared among the other REs of the REgroup. That is, in the process of detecting the detection signals withrespect to the REs of the RE group, the numerical analysis algorithmuses the same preprocessing filter. The specific RE (or the first RE)may be defined as a “reference RE”, which is used to compute thepreprocessing filter and is not related to the order or index of the REin the RE group.

Accordingly, if channel correlation between REs in the group is large,the proposed MIMO receiver shares the preprocessing filter (810)generated from one RE among all of the REs of the RE group and thenumerical analysis algorithm generates the detection signals using thepreprocessing filter (820, 830 and 840). Accordingly, the sameperformance can be obtained with less complexity less than theconventional MIMO receiver. As channel correlation between the first REand another RE in the RE group increases, such iteration speedshortening effects increase.

FIGS. 9 and 10 are diagrams showing the concept of a process ofdetecting a detection signal at a MIMO receiver using a preprocessingfilter. FIG. 9 shows a process of detecting a detection signal of a MIMOreceiver operating according to a method of sharing a received signaldetector (or a reception filter) and FIG. 10 is a process of detecting adetection signal of a MIMO receiver operating according to a method ofsharing the above-described preprocessing filter. In FIGS. 9 and 10, anarrow means an iterative computation process of a numerical analysisalgorithm.

First, in FIG. 9, the center 920 of circles means a desired value, thatis, an accurate value, of the MIMO receiver. If a detection value isslightly different from the accurate value (910), the numerical analysisalgorithm may reach the accurate value (920) through several iterativeprocesses. In contrast, if a detection signal is relatively close to theaccurate value (930 and 940), the accurate value (920) can be found witha smaller iteration number (950). Accordingly, the MIMO receiveroperating according to the reception filter sharing method operates toshare the reception filter such that the initial value of the detectionsignal becomes close to the accurate value (that is, an errordecreases).

In contrast, in FIG. 10, the MIMO receiver operating according to thepreprocessing filter sharing method operates to decrease the iterationnumber instead of enabling the initial value of the detection signal tobecome close to the desired value (that is, the center 1020 of thecircles). That is, the MIMO receiver according to the proposed methodcan find the desired value with a relatively smaller iteration number(1030) as compared to FIG. 9 even when an initial value significantlydifferent from the desired value 1020 of the numerical analysisalgorithm is computed (1010). In other words, in FIG. 10, the MIMOreceiver operates to rapidly increase the convergence speed according tothe iterative computation of the numerical analysis algorithm so as todecrease complexity.

Hereinafter, an embodiment in which such a MIMO receiver generates thepreprocessing filter v₁ will be described in detail.

According to a first embodiment, the preprocessing filter may begenerated by various algorithms such as a Jacobi method, a Gauss-Siedelmethod, an SQR preconditioning method and an incomplete Choleskyfactorization method.

First, an arbitrary matrix A₁ may be defined based on the MIMO channelof the reference RE (first RE) as shown in Equation 4 below.A ₁ =G ₁ ^(†) G ₁ +R  Equation 4

Since the matrix A₁ is a positive definite matrix and is symmetric,Equation 4 may be factorized as shown in Equation 5 below.A ₁ =L ₁ +D ₁ +L ₁ ^(H)  Equation 5

In Equation 5, L₁ denotes a lower triangular matrix and D₁ denotes adiagonal matrix. In Equation 5, the preprocessing filter V₁ according tothree methods among the above-described various methods may be defined.

Jacob method: V₁=D₁ ⁻¹

Gauss-Siedel method: V₁=(L₁+D₁)⁻¹

SQR preconditioning method: V₁=w(L₁+wD₁)⁻¹ (w is an arbitrary constant)

Among the above-described methods, the Gauss-Siedel method and the SQRpreconditioning method may clearly express the preprocessing filter V₁by computing an actual inverse matrix. However, in order to reducecomputational complexity for obtaining the inverse matrix, V₁ may becomputed through a back substitution process according to Equation 6below, without accurately computing V₁.x=V ⁻¹ y→Vx=y  Equation 6

In Equation 6, if V is a lower triangular matrix, x which is the valueof Equation 6 may be sequentially computed from the right equation ofEquation 6.

In addition to the above-described three methods, if the incompleteCholesky factorization method is applied, A₁ of Equation 5 may befactorized to an incomplete Cholesky factor {circumflex over (L)}₁, asshown in Equation 7 below. {circumflex over (L)}₁ is a lower triangularmatrix.A ₁ ≈{circumflex over (L)} ₁ {circumflex over (L)} ₁ ^(H)  Equation 7

Although the incomplete Cholesky factorization method may factorize A₁with complexity less than that of the complete Cholesky factorizationmethod, an approximated lower triangular matrix is defined. In theincomplete Cholesky factorization method, the preprocessing filter V₁ isdefined as Equation 8 below.V ₁=({circumflex over (L)} ₁ ^(H))⁻¹ {circumflex over (L)} ₁ ³¹¹  Equation 8

The preprocessing filter V₁ according to Equation 8 may be accuratelyexpressed by directly computing an inverse matrix or may be computed andexpressed by a back substitution process.

The preprocessing filter V₁ according to the embodiment of the presentinvention may be computed and defined according to various methods inaddition to the above-described methods. For example, various methodsand algorithms disclosed in “Iterative Methods for Sparse LinearSystems” may be used for the process of computing the preprocessingfilter V₁.

As a second embodiment of generating the preprocessing filter, thepreprocessing filter V₁ may be generated using the properties of theMIMO channel of the RE. In order to compute A₁ according to theabove-described first embodiment, a matrix X matrix operation process G₁^(†)G₁ is required. In order to improve computational complexity of suchan operation process, in the second embodiment, the MIMO channel of theRE is used to compute A₁ with low complexity.

More specifically, in the reference RE, G₁ ^(†)G₁ may be approximated toa diagonal matrix Z₁ of Equation 9 below.

$\begin{matrix}{{Z_{1}\overset{\Delta}{=}{\begin{bmatrix}{g_{1}^{H}g_{1}} & 0 & \ldots & 0 \\0 & {g_{2}^{H}g_{2}} & \ddots & \vdots \\\vdots & \ddots & \ddots & 0 \\0 & \ldots & 0 & {g_{N_{2}}^{H}g_{N_{2}}}\end{bmatrix} \approx {G_{1}^{\dagger}G_{1}}}}{G_{1} = \left\lbrack {g_{1}\mspace{14mu} g_{2}\mspace{14mu}\ldots\mspace{14mu} g_{N_{1}}} \right\rbrack}} & {{Equation}\mspace{14mu} 9}\end{matrix}$

Approximation of Equation 9 becomes accurate when the number N_(s) ofstreams increases and correlation between channel elements decreases. Insuch approximation, off-diagonal terms may be approximated to 0according to the properties of the channel in the massive MIMOenvironment. According to the above-described approximation process, thematrix A₁ may be defined by the diagonal matrix of Equation 10.A ₁ =Z ₁ +R  Equation 10

Subsequently, since A₁ of Equation 10 may be expressed only by diagonalelements, the Jacobi method described in the first embodiment isapplicable to A₁ of Equation 10 to compute the preprocessing filter V₁.In the second embodiment, if an error is large in the approximationprocess, the decrement of the iteration number of the numerical analysisalgorithm may not be large. That is, the speed of converging on thedesired value may not increase.

Subsequently, a third embodiment of generating a preprocessing filterwill be described with reference to FIG. 11. FIG. 11 is a diagramshowing an example of generating a preprocessing filter at a MIMOreceiver in relation to the present invention.

In the third embodiment, Z₁ having a small difference from G₁G₁ ^(†) ofthe first embodiment is found and the method proposed in the secondembodiment is used. For example, if the MIMO channel matrix G₁ isapproximated to a matrix {tilde over (G)}₁ having shapes 1110, 1120 and1130 shown in FIG. 11, it is possible to significantly reducecomputational complexity of A₁. In FIG. 11, a black element indicates anon-zero value and a white element indicates a zero value. That is, thevalue of each element of the channel matrix is compared with apredetermined threshold to approximate the value of the element lessthan the threshold to 0. At this time, the rank of the approximated{tilde over (G)}₁ should be equal to G₁.

The three embodiments of computing the preprocessing filter V₁ have beendescribed above. Hereinafter, a numerical analysis algorithm fordetecting a detection signal using a preprocessing filter will bedescribed.

The numerical analysis algorithm replaces inverse matrix operation ofMMSE, zero forcing (ZF), interference rejection combining (IRC), andBLAST algorithms for detecting and generating detection signals withrespect to an RE group. The proposed numerical analysis algorithm isapplicable to all MIMO receivers described in TR 36.866 for NAIC v1.1.0.Such a numerical analysis algorithm replaces only the above-describedinverse matrix operation and thus has detection performance equal orsimilar to that of the conventional MIMO receiver while improvingcomplexity.

As the numerical analysis algorithm, a conjugate gradient (CG)algorithm, a Newton method algorithm or a steepest descent methodalgorithm may be used. In the numerical analysis algorithm, a value iscalculated with a small iteration number (that is, at a high speed)using the above-described preprocessing filter V₁ and the effect ofreducing the iteration number increases as correlation between areference RE for generating a preprocessing filter and another REincreases.

For example, using FIG. 8 and the CG algorithm, the numerical analysisalgorithm will be described in detail. The CG algorithm is a convergingalgorithm for iteratively performing an operation until predeterminedaccuracy is derived. As the algorithm is iterated, a result having asmaller error is derived.

First, a MIMO receiver groups a plurality of REs having correlationequal to or greater than a predetermined value to form one RE groupshown in FIG. 6. Any one RE included in the RE group becomes a referenceRE (first RE) and the MIMO receiver generates a preprocessing filterusing the MIMO channel of the reference RE. Although the reference RE ofthe RE group may be closest to the center on the time/frequency axis,the present invention is not limited thereto.

The MIMO receiver generates detection signals Ŝ_(l) with respect to theother REs of the RE group using the numerical analysis algorithm (CGalgorithm) based on the preprocessing filter V₁ of the reference RE. TheCG algorithm may be implemented in the form of Equation 11 below.

$\begin{matrix}{{{\hat{s}}^{(0)} = I_{N_{s} \times 1}}{t = {{G_{l}^{H}G_{l}{\hat{s}}^{(0)}} + {R\;{\hat{s}}^{(0)}}}}{b = {G_{l}^{H}y_{l}}}{g^{(0)} = {b - t}}{d^{(0)} = {V_{l}g^{(0)}}}{{{while}\mspace{14mu}{g^{(i)}}} > {\delta{g^{(0)}}\mspace{14mu}{do}}}{t = {\left( g^{(i)} \right)^{\dagger}V_{1}g^{(i)}}}{t = {{G_{l}^{H}G_{l}d^{(i)}} + {Rd}^{(i)}}}{\alpha^{(i)} = \frac{t}{\left( d^{(i)} \right)^{\dagger}t}}{{\hat{s}}^{({i + 1})} = {s^{(i)} + {\alpha^{(i)}d^{(i)}}}}{g^{({i + 1})} = {g^{(i)} + {\alpha^{(i)}t}}}{\beta^{({i + 1})} = \frac{\left( g^{({i + 1})} \right)^{\dagger}V_{1}g^{({i + 1})}}{t}}{d^{({i + 1})} = {{V_{1}g^{({i + 1})}} + {\beta^{({i + 1})}d^{(i)}}}}{{end}\mspace{14mu}{while}}{{\hat{s}}_{l} = {\hat{s}}^{({end})}}} & {{Equation}\mspace{14mu} 11}\end{matrix}$

In Equation 11, Ŝ^((i)) is an estimated transmission signal in i-thiteration of the numerical analysis algorithm. The transmission signalof the 0^(th) iteration, that is, an initial value ŝ⁽⁰⁾, is set to avector composed of all entries of 1. ĝ⁽¹⁾, {circumflex over (d)}⁽¹⁾ andb^((i)) denote temporary vectors for obtaining a value and f₁, f₂ denotefunctions for determining a relation between the temporary vectors. Thevector ĝ^((i)) is a gradient vector and indicates a fastest direction inwhich the iterative algorithm converges on an accurate value. At thistime, if a difference between the updated vector g^((i)) and theinitially generated vector g⁽⁰⁾ is less than a predetermined threshold,algorithm iteration is stopped. That is, through the size of the vectorĝ^((i)), a difference between a result obtained by directly calculatinga MMSE filter and a secondary signal may be indirectly confirmed. If theg^((i)) value is 0, a difference between the secondary signal and theresult obtained using the MMSE is 0.

In Equation 11, δ determines an end time of the algorithm and may meantarget accuracy of the algorithm. δ may be automatically determined by asystem or may be determined according to user input. As δ decreases, analgorithm iteration number increases and the accuracy of a resultincreases and, as δ increases, an algorithm iteration number decreasesand the accuracy of a result decreases. That is, an allowable errorbetween a value obtained using the CG algorithm and a value obtainedusing the MMSE filter is determined according to the level of δ. TheMIMO receiver may control δ to provide trade-off between complexity andperformance. Meanwhile, in the CG algorithm, if an iteration numberbecomes equal to the size of a square matrix, a value obtained throughthe CG algorithm and a value obtained using the MMSE filter become equalto each other.

According to one embodiment, the MIMO receiver may restrict theiteration number of the numerical analysis algorithm to restrict amaximum time required to detect the detection signal. If a time requiredfor the MIMO receiver to detect the signal of a specific RE isrelatively greater than a time required to detect the signal of anotherRE, the total processing time of the system is influenced. In order toprevent such a problem, the time required to detect the detection signalmay be restricted to a specific range.

The detection signal may be restricted by restricting the iterationnumber of the numerical analysis algorithm. That is, since a timerequired for iteration of the numerical analysis algorithm is constant,the MIMO receiver may control an iteration time by restricting theiteration number. Restricting the iteration number may increase an errorbetween the value obtained through the CG algorithm and the valueobtained using the MMSE filter. There is a trade-off between performancedeterioration and a processing time.

FIG. 12 is a diagram showing a MIMO receiver operational process ofanother embodiment using a preprocessing filter. In FIG. 12, anotherembodiment of generating a preprocessing filter V₁ will be described.

In FIG. 12, the preprocessing filter V₁ is computed using the channelsof all of the REs of the RE group. For example, V₁ may be generatedbased on G_(A) computed in Equation 12 below.

$\begin{matrix}{G_{A} = {\frac{1}{N}{\sum\limits_{l = 1}^{N}{w_{l}G_{l}}}}} & {{Equation}\mspace{14mu} 12}\end{matrix}$

In Equation 12, N denotes the number of REs in the RE group and w_(l)denotes a weight of each channel matrix. In the case of w_(l)=1, G_(A)is defined as an average of all channel matrices. The MIMO receivercomputes the preprocessing filter V₁ to be shared in the RE group basedon the channel matrix G_(A) computed in Equation 12 (1210).Subsequently, the MIMO receiver detects the detection signal of each REusing the preprocessing filter V₁ (1220, 1230 and 1240).

The embodiment in which the MIMO receiver generates the preprocessingfilter V₁ and the embodiment in which the detection signal is generatedusing V₁ have been described with reference to FIGS. 8 to 12.Hereinafter, an embodiment in which a reception filter is shared in anRE group will be described with reference to FIGS. 13 to 15, in additionto an embodiment in which a preprocessing filter is shared in an REgroup.

FIG. 13 is a diagram showing a MIMO receiver operational process ofanother embodiment using a preprocessing filter. In FIG. 13, unlike FIG.8, the MIMO receiver generates a preprocessing filter V₁ and a receptionfilter B₁ based on a channel G₁ of a reference RE of an RE group (1310).V₁ and B₁ are shared among all of the REs of the RE group and the MIMOreceiver detects a primary signal from a received signal using thereception filter B₁ (1320 and 1330). Subsequently, the MIMO receiverdetects a secondary signal through a process of compensating for theprimary signal using the preprocessing filter V₁ and the numericalanalysis algorithm based on a unique channel of each RE (1340, 1350 and1360).

The above-described process will be described in detail with referenceto Equation 13 below.

$\begin{matrix}{{b = {G_{l}^{H}y_{l}}}{{\hat{s}}^{(0)} = {B_{1}b}}{t = {{G_{l}^{H}G_{l}{\hat{s}}^{(0)}} + {R\;{\hat{s}}^{(0)}}}}{g^{(0)} = {b - t}}{d^{(0)} = {V_{1}g^{(0)}}}{{{while}\mspace{14mu}{g^{(i)}}} > {\delta{g^{(0)}}\mspace{14mu}{do}}}{t = {\left( g^{(i)} \right)^{\dagger}V_{1}g^{(i)}}}{t = {{G_{l}^{H}G_{l}d^{(i)}} + {Rd}^{(i)}}}{\alpha^{(i)} = \frac{t}{\left( d^{(i)} \right)^{\dagger}t}}{{\hat{s}}^{({i + 1})} = {{\hat{s}}^{(i)} + {\alpha^{(i)}d^{(i)}}}}{g^{({i + 1})} = {g^{(i)} - {\alpha^{(i)}t}}}{\beta^{({i + 1})} = \frac{\left( g^{({i + 1})} \right)^{\dagger}V_{1}g^{({i + 1})}}{t}}{d^{({i + 1})} = {{V_{1}g^{({i + 1})}} + {\beta^{({i + 1})}d^{(i)}}}}{{end}\mspace{14mu}{while}}{{\hat{s}}_{l} = {\hat{s}}^{({end})}}} & {{Equation}\mspace{14mu} 13}\end{matrix}$

In Equation 13, ŝ_(l) ⁽⁰⁾ denotes a primary signal detected from thereceived signal of an l-th RE using the reception filter B₁ generatedbased on the channel of the reference RE. The numerical analysisalgorithm of Equation 13 compensates for the primary signal using thepreprocessing filter V₁ generated from the reference RE to generate thesecondary signal ŝ_(l). If correlation between the reference RE andanother RE of the RE group is large, the primary signal detected usingthe common reception filter B₁ is similar to a value directly obtainedusing the MMSE filter and the process of, at the numerical analysisalgorithm, compensating for the primary signal using the preprocessingfilter V₁ to detect the secondary signal is more rapidly performed. Incontrast, if correlation is small, an error between the primary signaland the value directly obtained using the MMSE filter is large and theprocess of detecting the secondary signal is little different from thatof the case where the preprocessing filter is not used.

Hereinafter, an embodiment of obtaining the preprocessing filter V₁ inthe embodiment of FIG. 13 will be described. In FIG. 13, unlike FIG. 8,since the common reception filter B₁ shared in the RE group is computed,the process of computing the preprocessing filter V₁ may be differentfrom the process of FIG. 8.

First, an arbitrary matrix A₁ is defined based on the channel of thereference RE as shown in Equation 14.A ₁ =G ₁ ^(H) G ₁ +R  Equation 14

In Equation 14, A₁ has an inverse matrix relation B₁=A₁ ⁻¹ with thecommon reception filter B₁. The MIMO receiver may define thepreprocessing filter V₁ based on the matrix A₁ according to thefollowing three embodiments.

First, the preprocessing filter V₁ may be the inverse matrix of thecommon reception filter B₁. That is, the common reception filter B₁ maybe the preprocessing filter V₁. This embodiment is expressed as shown inEquation 15 and, if the common reception filter B₁ is computed, the MIMOreceiver uses the common reception filter B₁ as the preprocessingfilter. Since the common reception filter and the preprocessing filterare the same, the MIMO receiver does not need to further compute V₁ anddoes not require a memory used to compute and store V₁.V ₁ =A ₁ ⁻¹ =B ₁  Equation 15

Second, the MIMO receiver may factorize A₁ according to the completeCholesky factorization method to compute the preprocessing filter V₁.Such a process is performed through three steps according to thefollowing order.A ₁ =L ₁ L ₁ ^(H)(L ₁ is a lower triangular matrix)  i)B ₁=(L ₁ ^(H))⁻¹ L ₁ ⁻¹  ii)V ₁=({circumflex over (L)} ₁ ^(H))⁻¹ {circumflex over (L)} ₁ ⁻¹ ,{circumflex over (L)} ₁ ≈L ₁  iii)

If a back substitution process is used, the process of obtaining theinverse matrix of the lower triangular matrix L₁ in step ii) may beomitted. That is, in the second method, when applying B₁ and V¹,complexity can be reduced using the back substitution process. In thiscase, the main complexity of the process of generating the preprocessingfilter V₁ and the common reception filter B₁ occurs in step i).

Step iii) is a step of generating a sparse preprocessing filter (amatrix, the most elements of which are 0) through approximation of{circumflex over (L)}₁≈L₁. In such a process, if the preprocessingfilter is a sparse filter, computational complexity is significantlyreduced per iteration of the numerical analysis algorithm.

In a third method, the preprocessing filter V₁ may be computed accordingto the incomplete Cholesky factorization method. Such a process isperformed through three steps according to the following order.A ₁ ≈{circumflex over (L)} ₁ {circumflex over (L)} ₁ ^(H) ({circumflexover (L)} ₁ is a lower triangular matrix)  i)B ₁=({circumflex over (L)} ₁ ^(H))³¹ ¹ {circumflex over (L)} ₁ ⁻¹  ii)V ₁=({circumflex over (L)} ₁ ^(H))⁻¹ {circumflex over (L)} ₁ ⁻¹  iii)

In the second embodiment, the main complexity of the process ofgenerating the preprocessing filter V₁ and the common reception filterB₁ occurs in step i). Accordingly, in the third embodiment, instead ofusing the complete Cholesky factorization in step i), {circumflex over(L)}₁ is computed using incomplete Cholesky factorization.

If the preprocessing filter V₁ and the common reception filter B₁ arecomputed based on {circumflex over (L)}₁, unlike the second embodiment,even the secondary signal of the reference RE should be computed throughthe compensation process. This is because B₁ is an approximated inversematrix and thus an error may occur in the reference RE. As a result, thethird embodiment requires lowest complexity upon generating the commonreception filter and the preprocessing filter but requires a largestiteration number in the compensation process.

The above-described embodiments are merely exemplary and thepreprocessing filter and the common reception filter may be definedaccording to various methods, in addition to the above-describedmethods.

Unlike the embodiment described with reference to FIG. 13, thecompensation process 1340 and 13450 using the unique channel of the REand the preprocessing filter may be omitted according to channelcorrelation between REs. That is, if correlation between the referenceRE and another RE is sufficiently large, the error of the primary signaldetected using the common reception filter B₁ is relatively small. Ifinfluence of the error of the primary signal of the RE on performance ofa final result is predicted to be low, the process of compensating forthe primary signal is omitted and the primary signal is immediatelyinput to a decoder 1370. That is, it is possible to reduce computationalcomplexity and memory requirements necessary for the compensationprocess.

FIG. 14 is a diagram showing another MIMO receiver operational processusing a preprocessing filter. FIG. 14 is similar to FIG. 13 in that thecommon reception filter B₁ is used. However, in the embodiment of FIG.14, the preprocessing filter V₁ is not computed based on the channel ofthe reference RE but the preprocessing filter of each RE is computedusing the unique channel of each RE of the RE group. The process ofcompensating for the primary signal is performed using the preprocessingfilter generated based on the unique channel of each RE instead of V₁.

More specifically, the MIMO receiver computes the common receptionfilter B₁ based on the channel of the reference RE (1410). B₁ is sharedamong the REs of the RE group and is used to generate the primary signal(1430). Prior to the process of compensating for the primary signal, theMIMO receiver generates a preprocessing filter based on the uniquechannel of each RE (1440 and 1460). That is, V_(z) is computed based onG_(z) with respect to a second RE (1440) and V_(N) is computed based onG_(N) with respect to an N-th RE.

The embodiments described with reference to FIGS. 8 to 13 are applicableto the process of the unique preprocessing filter of each RE.Subsequently, the MIMO receiver performs the compensation process basedon the numerical analysis algorithm using the unique preprocessingfilter generated with respect to each RE (1450 and 1470). The secondarysignal generated through the compensation process (1480) is input to andprocessed in the decoder 1490.

According to the embodiment of FIG. 14, since the preprocessing filteris generated per RE, additional complexity is required. However, ifchannel correlation between REs is low, the iteration number of thecompensation process increases in the embodiment in which thepreprocessing filter is shared according to the methods of FIGS. 8 to13. The embodiment in which the unique preprocessing filter is used asshown in FIG. 14 is more efficient at reducing complexity and a timerequired for a computational process.

Further, if the preprocessing filter is generated according to theJacobi method, the Gauss-Siedel method and the SQR preconditioningmethod assuming the back substitution process, complexity increased inthe process of computing the preprocessing filer is minimized and alarge burden is not imposed on the MIMO receiver. If the lowertriangular matrix having a size of N is subjected to the backsubstitution process, complexity is less than N².

FIG. 15 is a graph showing comparison between computational complexitiesof the prior art and the case of applying the preprocessing filter.

In the graph of FIG. 15, a curve denoted by a rectangle showscomputational complexity when signals are detected with respect to allof the REs of the RE group using respective MMSE filters. A curvedenoted by a star shows the case where the preprocessing filter V₁ isshared in the RE group and a curve denoted by a triangle shows the casein which V₁ is not shared in the RE group and the common receptionfilter B₁ is shared to perform the compensation process. In FIG. 15, itcan be seen that the above-proposed MIMO receiver has higher complexitygain as the number of received streams increases.

According to the above-described embodiments, if correlation among allof the REs of the RE group is 1, the reception filter B_(l) of each REbecomes equal to the reception filter B₁ of the reference RE.Accordingly, even when only B₁ is used, the primary signal may be inputto the decoder without performance deterioration. Therefore, since onlyone reception filter is obtained with respect to the RE group, the totalcomputational complexity is reduced to 1/N (N being the number of REs ofthe RE group).

If correlation among the REs of the RE group is less than 1, the errorof the primary signal computed using the common reception filter B₁ iscompensated for using the preprocessing filter V₁. As correlationbetween the REs increases, the compensation process of the numericalanalysis algorithm using the preprocessing filter is rapidly performed(that is, the iteration number decreases). At this time, thecompensation process using the preprocessing filter has highercomputational complexity than the compensation process without thepreprocessing filter but has a significantly lower iteration number thanthe compensation process without the preprocessing filter. As a result,the proposed MIMO receiver makes the best of the correlation between theREs, thereby reducing complexity while minimizing performancedeterioration.

The MIMO receiver can further reduce computational complexity at thesacrifice of performance deterioration due to an error in thecompensation process using the preprocessing filter, thereby providing atrade-off between computational complexity and performance.

In addition, according to the proposed method, the inverse matrix is notdirectly computed with respect to the REs except for the reference REand all operations are performed by a matrix X vector operation. Adistributed processing method is not easily applicable to the inversematrix operation, but is easily applicable to the matrix X vectoroperation due to easy parallelization. Therefore, overall processingtime can be rapidly reduced.

3. Proposed Method of Operating MIMO Receiver

The process of, at the MIMO receiver, processing the received signalsusing the preprocessing filter has been described above. Hereinafter, aprocess of, at the MIMO receiver, forming an RE group which is a unitfor processing the received signals will be described.

First, the concepts of an RE group, a reference RE and a normal RE willbe described. FIGS. 16 to 20 are diagrams showing a process of formingRE groups according to an embodiment of the present invention.Rectangles shown in FIGS. 16 to 20 indicate REs and hatched, patternedor colored rectangles indicate reference REs of RE groups. One or moreREs form an RE group and the REs included in the RE group share areception filter and/or a preprocessing filter generated based on thechannel information of the reference RE. That is, the reception filterand/or the preprocessing filter of the reference RE are directlycomputed from the received signal (e.g., using the MMSE filter).Hereinafter, the REs except for the reference RE of the RE group arereferred to as “normal REs”.

For example, in FIG. 16, RE group #1 1610 include 11*6=66 REs and REgroup #1 1610 is composed of one reference RE located at the centerthereof and 65 normal REs. Similarly, RE group #2 1620 is composed ofone reference RE and 65 normal REs. A distance from the reference RE toeach normal RE is defined by coordinate values of {frequency axis, timeaxis} and, for example, a normal RE located at A in RE group #1 1610 maybe expressed by {0, 2}. Normal REs located at B, C and D may beexpressed by {0, 5}, {−3, 0} and {−3, 5}, respectively. Such coordinatevalues are obtained by determining the right and up directions of the REgroup as frequency/time-axis increasing directions, which are merelyexemplary.

In FIG. 16, each of two RE groups 1610 and 1620 includes 66 REs and allof the REs included in the two RE groups 1610 and 1620 are referred toas a “mother group”. That is, hereinafter, the term “mother group” is aunit for processing a plurality of REs to form RE groups at the MIMOtransmitter. In FIG. 16, the mother group is one resource block (RB)including 11*12=132 REs (in FIG. 16, the MIMO receiver processes an RBwhich is a mother group to form two RE groups.

In the embodiments shown in FIGS. 16 to 20, the same mother group isdivided according to different methods to form RE groups. The mothergroup is not limited to the implementations of FIGS. 16 to 20 and themother group may be defined in slot, subframe, subband or frame units inLTE/LTE-A, instead of the RB.

As described above, the MIMO receiver generates a reception filter and apreprocessing filter to be shared in an RE group based on the channelinformation of a reference RE and shares the generated filters among thenormal REs to generate detection signals from the received signals. Atthis time, complexity of the reference RE required for reception filter,the preprocessing filter and data detection is expressed as shown inEquation 16 below.

$\begin{matrix}{{Cpx}_{RE\_ DMMSE} = {{\frac{3}{2}N_{s}{N_{r}\left( {N_{s} + 1} \right)}} + N_{s}^{3}}} & {{Equation}\mspace{14mu} 16}\end{matrix}$

In addition, complexity of the normal RE is expressed as shown inEquation 17 below.

$\begin{matrix}{{{Cpx}_{RE\_ Proposed}({iter})} = {{\frac{1}{2}N_{s}{N_{r}\left( {N_{s} + 3} \right)}} + {4N_{s}^{2}} + {\frac{3}{2}N_{s}} + {\left( {{iter} - 1} \right)\left( {N_{s}^{2} + {\frac{7}{2}N_{s}}} \right)}}} & {{Equation}\mspace{14mu} 17}\end{matrix}$

From Equation 17, it can be seen that complexity required to generatethe detection signal may be influenced by the iteration number of thealgorithm. In addition, from Equations 16 and 17, total complexityrequired to process one mother group is expressed as shown in Equation18 below.

$\begin{matrix}{{Cpx}_{Total} = {{N_{RE\_ DMMSE} \times {Cpx}_{RE\_ DMMSE}} + {\sum\limits_{{iter} = 1}^{{iter}_{\max}}{{N_{RE\_ Proposed}({iter})} \times {{Cpx}_{RE\_ Proposed}({iter})}}}}} & {{Equation}\mspace{14mu} 18}\end{matrix}$

In Equation 18, N_(RE) _(_) _(DMMSE) denotes the number of reference REslocated in the mother group and N_(RE) _(_) _(Proposed) (iter) denotesthe number of normal REs, for which the iteration number of thenumerical analysis algorithm is ‘iter’ in the mother group.

In RE group #1 1610 of FIG. 16, the iteration number for the normal RElocated at B may be greater than the iteration number for the normal RElocated at A. This is because effectiveness of the reception filterand/or the preprocessing filter shared in the RE group decreases as thedistance from the reference RE increases and the iteration number of thenumerical analysis algorithm for finding a value increases. In addition,as channel change increases (if a power delay profile is large orDoppler effect is large), effectiveness of the shared filters rapidlydecreases. Accordingly, for a normal RE located far from the referenceRE, if channel change is large, the iteration number of the algorithmsignificantly increases, significantly increasing total complexityCpx_(Total) required to generate the detection signal.

Hereinafter, a method of minimizing complexity even when complexityrequired to generate the detection signal increases by proposing variousembodiments in which a MIMO receiver forms RE groups from a mother groupwill be described.

First, if the MIMO receiver uses RE groups #1 and #2 1610 and 1620 shownin FIG. 16 and channel correlation between REs is very high (forexample, a pedestrian channel (3 km/h) if the length of a power delayprofile is short and the Doppler effect has a small value),effectiveness of the reception filter and the preprocessing filtershared in the RE group is very high. Accordingly, the iteration numberof the numerical analysis algorithm for all of the REs of the RE groupmay be 1 and computational complexity Cpx_(Total) required to processthe mother group can be minimized.

In contrast, if channel correlation between REs is low, the numericalanalysis algorithm needs to be iterated for normal REs located far fromthe reference RE. For example, assume that the iteration numbers for theREs located at A, B, C and D are 1, 2, 3 and 4, respectively. Increasingthe number of normal REs having a large iteration number increasescomputational complexity of the RE group.

In order to solve the problem that the number of normal REs having alarge iteration number increases, for example, four RE groups 1710,1720, 1730 and 1740 shown in FIG. 17 are assumed. Since the four REgroups 1710, 1720, 1730 and 1740 shown in FIG. 17 are equal to theembodiment of FIG. 16 in terms of the mother group, REs equal in numberto the number of REs of the two RE groups 1610 and 1620 are included.However, in the four RE groups 1710, 1720, 1730 and 1740 shown in FIG.17, a distance from the reference RE to a farthest normal RE in each REgroup is shorter. Accordingly, the iteration numbers for some normal REscan be reduced.

For example, the distances between the normal REs located at A and B andthe reference RE are respectively {0, 2} and {0, 5} in FIG. 16, whereasthe distances between the normal REs located at A and B and thereference RE are respectively reduced to {0, 1} and {0, 2} in FIG. 17.In this case, the iteration numbers for arbitrary REs located at A, B, Cand D can be reduced.

As a result, although the total number of reference REs increases from 2to 4 in FIG. 17, total complexity required to process the mother groupis reduced as compared to FIG. 16.

From the above-described embodiment, the number of reference REs and theconfiguration of the RE group are determined so as to minimizeCpx_(Total), that is, computational complexity required to process allof the REs included in the mother group. The “configuration” of the REgroup means the size and shape of the RE group. FIGS. 17 and 20 areequal in that the number of RE groups is 4 and are different in theshape thereof. Thus, the configurations of the RE groups are differentfrom each other.

There are various methods of forming the RE groups. Hereinafter,embodiments in which RE groups are formed so as to minimizecomputational complexity of the mother group will be described.

First, the reference RE in the RE group is located at a place where amaximum distance from a normal RE is minimized. In other words, thereference RE should be located in the RE group such that a distance(that is, a maximum distance) between the reference RE to a farthestnormal RE is minimized.

As described above, a distance between REs in the RE group may beexpressed by {frequency axis, time axis} using two elements including afrequency axis and a time axis. The iteration number required togenerate the detection signal of a normal RE far from the reference REis equal to or greater than that of a normal RE close to the referenceRE. Accordingly, if the reference RE is arranged according to theproposed method, it is possible to prevent the iteration number for aspecific normal RE from rapidly increasing.

In RE group #1 1610 of FIG. 16, the maximum distance between thereference RE and the normal RE is {3, 5}. Even when any other RE of REgroup #1 1610 is selected as a reference RE, this maximum distancecannot be reduced. In contrast, in FIG. 18, a reference RE is located ata corner of each RE group and a maximum distance is {5, 10}.Accordingly, the normal RE located at A of FIG. 18 requires a largeriteration number as compared to the normal RE located at the sameposition A of FIG. 16. Further, as channel correlation between REs inthe RE group decreases, the iteration number significantly increases,the number of normal REs requiring a large iteration number rapidlyincreases, and total complexity Cpx_(Total) increases. In conclusion, asdescribed above, the reference RE should be located at a place where themaximum distance from a normal RE is minimized.

Next, the MIMO receiver may determine the configuration of the RE groupbased on channel correlation between REs. Channel correlation betweenREs has influence on the iteration number required to generate thedetection signal of the normal RE, as described above. For example, ifchannel correlation between REs is high, the iteration number requiredfor the MIMO receiver to detect data of the normal RE is small. Incontrast, if channel correlation between REs is low, the iterationnumber required for the MIMO receiver to detect data from the normal REunder the same condition is large. This is because the MIMO receiveruses the reception filter and the preprocessing filter to detect data ofthe RE group and the effects of the shared filters increase as thechannel correlation increases.

More specifically, the MIMO receiver knows channel information of allREs in advance and the channel correlation between REs may be computedaccording to Equation 19 based on the channel information of the REs.

$\begin{matrix}{{\beta_{k}^{(f)} = {\frac{1}{C}{\sum\limits_{l \Subset C}{\frac{1}{2{G_{l}^{\dagger}}_{F}^{2}}\left( {{{{diag}\left\{ {G_{l}^{\dagger}G_{i,k}^{(f)}} \right\}}} + {{{diag}\left\{ {G_{l}^{\dagger}G_{l,{- k}}^{f}} \right\}}}} \right)}}}}{\beta_{k}^{(t)} = {\frac{1}{C}{\sum\limits_{l \Subset C}{\frac{1}{2{G_{l}^{\dagger}}_{F}^{2}}\left( {{{{diag}\left\{ {G_{l}^{\dagger}G_{i,k}^{(t)}} \right\}}} + {{{diag}\left\{ {G_{l}^{\dagger}G_{l,{- k}}^{t}} \right\}}}} \right)}}}}} & {{Equation}\mspace{14mu} 19}\end{matrix}$

In Equation 19, ∥•∥_(F) ² indicates the Frobenius norm according to theFrobenius method. In addition, a function diag(B) indicates a diagonalmatrix obtained by extracting only the diagonal elements of a matrix B.C and |C| indicate the index set of an arbitrary RE which is used as areference RE used to compute channel correlation in the RE group and thesize of the index set, respectively. For example, in the embodiment ofFIG. 16, C may indicate indices of two reference REs of RE group #1 1610and RE group #2 1620 or indices of two arbitrary REs which are notreference REs. In addition, |C| is 2.

In FIG. 16, if the channel of the reference RE of RE group #2 1620 isG_(l), l ∈ C, G_(l,1) ^((f))/G_(l,−1) ^((f)) indicate the channels ofthe normal REs located at E and F separated from the reference RE on thefrequency axis by distances 1 and −1, respectively. Similarly, G_(l,1)^((t))/G_(l,−1) ^((t)) indicate the channels of the normal REs locatedat G and H separated from the reference RE on the time axis by distances1 and −1, respectively.

Meanwhile, β_(k) ^((f)) indicates channel correlation between thereference RE G_(l), l ∈ C and the normal RE G_(l,k) ^((f)) separatedfrom the reference RE by k on the frequency axis. If the channels ofG_(l) and G_(l,k) ^((f)) are the same, β_(k) ^((f)) is 1 and, otherwise,is less than 1. The MIMO receiver may compute the channel correlationbetween REs along the frequency axis based on β_(k) ^((f)) of Equation19, and the maximum distance on the frequency axis in the RE group isdetermined according to the computed channel correlation and Equation 20below.k=0, β₀ ^((f))=1while β_(k) ^((f))>γ dok=k+1compute β_(k) ^((f))end whilek _(max) ^((f)) =k−1  Equation 20

In Equation 20, k_(max) ^((f)) denotes the maximum distance on thefrequency axis, γ denotes a minimum threshold of β_(k) ^((f)) which isthe channel correlation between REs on the frequency axis and has avalue less than 1. In Equation 20, if β_(k) ^((f)) is less than γ, themaximum distance from the reference RE on the frequency axis is k−1,that is, k_(max) ^((f))=k−1. Equation 20 means that up to an RE justbefore channel correlation with the reference RE becomes a minimumthreshold is determined as an RE group in which thereception/preprocessing filter is shared. Both ends of the RE group onthe frequency axis are determined according to Equation 20.

In Equation 19, channel correlation between REs on the time axis may becomputed as β_(k) ^((t)), and the maximum distance on the time axis isalso determined through a method similar to Equation 20. Thus, both endsof the RE group on the time axis may be determined, and theconfiguration (that is, shape and size) of the RE group is finallydetermined when the maximum distances on the two axes are determined.That is, the reception/preprocessing filter is shared up to a distancewhere correlation with the reference RE becomes equal to or greater thanthe threshold on the frequency and time axis.

As another embodiment, the MIMO receiver may predict a power delayprofile of a channel using a common reference signal (CRS). Such a powerdelay profile indicates the impulse response of the channel in the timedomain and, as the length thereof increases, channel change on thefrequency axis increases. The MIMO receiver may compute the maximumdistance described in Equation 20 from the power delay profile of thechannel.

More specifically, if the channel delay profile is very long, themaximum distance k_(max) ^((f)) of Equation 20 may become 1. Incontrast, if the channel delay profile is short, all the channels on thefrequency axis become equal and the maximum distance may be set to 6.That is, the MIMO receiver may determine the maximum distance betweenthe reference RE and the normal RE on the frequency axis using thechannel delay profile.

In addition, the MIMO receiver supporting LTE/LTE-A may measure theDoppler effect and determine the maximum distance on the time axis usingthe Doppler effect similarly to the above-described power delay profile.As a result, the MIMO receiver may determine the configuration of the REgroup using the power delay profile and the Doppler effect.

According to another embodiment, the MIMO receiver may determine anerror allowable coefficient of the numerical analysis algorithm based onat least one of the signal to noise ratio (SNR)/signal to interferenceratio (SIR)/signal to interference plus noise ratio (SINR) of thereceived signal and determine the configuration of the RE group. Theerror allowable coefficient δ of the numerical analysis algorithm wasdescribed above in Equation 11 and indicates the error allowable rangeof the computed result value of the numerical analysis algorithm.

Such an error means a difference between a result of direct computationusing the MMSE filter for the normal RE and a result of computationusing the shared reception/preprocessing filter according to theproposed method. Accordingly, as the error allowable coefficient δincreases, a probability of increasing an error of the result ofcomputation increases and thus performance of the proposed MIMO receiverdeteriorates. However, if the SNR/SIR/SINR is low, noise or interferencedominantly influences on performance rather than the error. Accordingly,in this case, although the error allowable coefficient is large,performance deterioration of the receiver is relatively low. If theerror allowable coefficient increases, the iteration number of thenumerical analysis algorithm may be reduced, thereby reducingcomputational complexity required to generate the detection signal. Ifthe error allowable coefficient is large, the iteration number of thenumerical analysis algorithm for every RE may be reduced and a larger REgroup may be formed as compared to the case where the error allowablecoefficient is small.

More specifically, for example, in the case of an RE located at D inFIG. 16, if δ=0.0001, the iteration number required for the numericalanalysis algorithm is 4. In contrast, δ=0.01, the iteration number maybe reduced to 2. Accordingly, if the SNR/SIR/SINR is not considered, theMIMO receiver should form the RE groups in the configuration shown inFIG. 17 instead of FIG. 16 in order to prevent the iteration number frombecoming 4. In contrast, if the SNR/SIR/SINR is considered, the MIMOreceiver may increase the error allowable coefficient δ to form the REgroups in the configuration shown in FIG. 16 if the SNR/SIR/SINR is low.

Further, the MIMO receiver may determine an average SINR per mothergroup and determine the error allowable coefficient of the mother groupbased on the average SINR as shown in Equation 21.δ=10^(−SINR/5)  Equation 21

For example, rectangles of FIG. 21 indicate mother groups 2110. Each ofthe mother groups 2111, 2112, 2113, 2114, 2114 and 2116 may be composedof a plurality of REs (e.g., an RB of FIGS. 16 to 20) and, in eachmother group, RE groups may be formed in the configurations of FIGS. 16to 20.

In FIG. 21, as a result of processing mother group #1 2111, an SINR maybe 10 dB. In this case, the MIMO receiver determines δ=0.01 with respectto mother group #1 2111 and forms RE groups in the configuration shownin FIG. 17. Subsequently, if the SINR is 15 dB as a result of processingmother group #2 2112, the MIMO receiver may determine δ=0.001 and formsmaller RE groups in the configuration of FIG. 19. Subsequently, if theSINR is 5 dB as a result of processing mother group #3 2113, the MIMOreceiver may determine δ=0.1 and form RE groups in the configuration ofFIG. 16. As a result, the MIMO receiver may actively determine theconfiguration of the RE group according to the SINR measured per mothergroup, thereby additionally reducing computational complexity requiredto process the received signal.

As another embodiment which uses the SNR/SIR/SINR, the MIMO receiver maydetermine a minimum threshold γ of the channel correlation inconsideration of the SNR/SIR/SINR. The minimum threshold γ means minimumcorrelation satisfied by the RE in order to share thereception/preprocessing filter based on the reference RE, as describedin Equation 20.

The MIMO receiver may decrease the minimum threshold of the channelcorrelation if the SNR/SIR/SINR is low. In this case, the algorithm ofEquation 20 may select a larger maximum distance k_(max) ^((f)) from thereference RE, thereby forming a larger RE group. In contrast, if theSNR/SIR/SINR is high, the MIMO receiver may increase the minimumthreshold. The algorithm of Equation 20 selects a smaller maximumdistance, thereby forming a smaller RE group.

As another embodiment, if the mother group is an RB, the MIMO receivermay determine RE groups in RB units and form RE groups in considerationof the iteration number of a previous RB. The MIMO receiver may form REgroups each having a size less than that of RE groups applied to theprevious RB with respect to a next RB, if the iteration number of thenumerical analysis algorithm exceeds a specific threshold in a processof generating detection signals of the previous RB.

More specifically, a specific threshold for the iteration number of thenumerical analysis algorithm is χ. For example, when two RE groups areformed with respect to the RB as shown in FIG. 16, the case where theiteration number of the numerical analysis algorithm exceeds thethreshold χ in the process of generating the detection signal of thenormal RE located at D of FIG. 16 may be considered. At this time, theMIMO receiver forms smaller RE groups in the configuration shown in FIG.17 with respect to the next RB. When the smaller RE groups are formed,the distance between the normal RE located at D and the reference RE isreduced from {3, 5} to {3, 2} and the iteration number of the numericalanalysis algorithm is reduced.

In contrast, if the iteration number does not exceed the threshold χ inthe process of generating the detection signals of the RE groups shownin FIG. 16, the RE groups having the configuration shown in FIG. 16 arecontinuously applicable to a next RB. As a result, the MIMO receiver mayreduce the RE group depending on whether the iteration number of thenumerical analysis algorithm for the previous RB exceeds the thresholdin the process of forming RE groups in RB units.

Reducing the RE group may mean any one of reduction in thefrequency-axis direction, reduction in the time-axis direction andreduction in the frequency- and time-axis directions. In the embodimentof FIG. 16, if the iteration number of the normal RE located at D ofFIG. 16 exceeds the threshold χ, the MIMO receiver may compare theconvergence speed of the normal RE located at C (the error of the casein which the common filter is not used) with the convergence speed ofthe normal RE located at B. If the convergence speed of the normal RElocated at C is higher than that of the normal RE located at B (that is,the error is low), the channel correlation between the reference RE andthe RE located at C is greater than the channel correlation between thereference RE and the RE located at B.

Therefore, the MIMO receiver may form the RE groups having theconfiguration shown in FIG. 17 in which the reference REs are furtherprovided in the time-axis direction with respect to the next RB (tofurther provide the reference REs at positions closer to B). Incontrast, if the convergence speed of the normal RE located at C islower (that is, the error is higher), the channel correlation betweenthe reference RE and the RE located at C is less than the channelcorrelation between the reference RE and the RE located at B. Therefore,the MIMO receiver may form RE groups having the configuration shown inFIG. 20 with respect to the next RB (to further provide the referenceREs at a position closer to C).

The method of controlling the configuration of the RE group inconsideration of the convergence speed may be understood as consideringthe iteration number of the numerical analysis algorithm. Meanwhile, ahigh convergence speed (a higher speed for reducing the error periteration number) means that the iteration number required to generatethe detection signal is small.

The convergence speed (that is, the error of the case where the commonfilter is not used) may be confirmed by computing ∥g^((i))∥ (that is,the gradient) of “while ∥g^((i))∥>δ∥g⁽⁰⁾∥ do” of the numerical analysisalgorithm described in Equation 11. In other words, as ∥g⁽¹⁾∥ decreaseswith respect to the same iteration number i, the convergence speedincreases (that is, the error decreases). Therefore, by comparing∥g^((i))∥ of the RE located at C and ∥g^((i))∥ of the RE located at D,the convergence speeds of the two REs may be compared.

The embodiment in which the MIMO receiver compares the iteration numberof the numerical analysis algorithm and the error to determine theconfiguration of the next RE group has been described above.Hereinafter, in addition to the above description, an embodiment inwhich the MIMO receiver predetermines the configuration of the next REgroup using channel correlation between REs will be described.

The process of, at the MIMO receiver, measuring channel correlation forthe frequency axis and the time axis was described using Equation 19. Ifthe channel correlation in the frequency-axis direction is less thanthat in the time-axis direction (that is, if channel change in thefrequency-axis direction is larger), an RE group in which a maximumdistance in the frequency-axis direction is reduced may be selected. Incontrast, if the channel correlation in the time-axis direction is lessthan that in the frequency-axis direction (that is, if channel change inthe time-axis direction is larger), an RE group in which a maximumdistance in the time-axis direction is reduced may be selected.According to this embodiment, by reducing the maximum distance in theaxis direction in which the iteration number of the numerical analysisalgorithm is large due to low channel correlation, it is possible toreduce computational complexity of all of the RE groups with respect tothe next RB.

For example, if

β_(k_(max)^((f)))^((f)) > β_(k_(max)^((f)))^((t))is satisfied while the maximum iteration number of the numericalanalysis algorithm exceeds χ while the RE group having the configurationshown in FIG. 16 is used, the MIMO receiver may form the RE groupshaving the configuration shown in FIG. 17 with respect to a next RB. Incontrast, if

β_(k_(max)^((f)))^((f)) < β_(k_(max)^((f)))^((t))is satisfied, the RE groups having the configuration shown in FIG. 20may be formed.

The embodiment in which the MIMO receiver compares the maximum iterationnumber with the threshold χ may be changed as follows.

Contrary to the embodiment in which the RE group is reduced, if themaximum iteration number of the numerical analysis algorithm in theprocess of detecting the data of the previous RB is less than thespecific threshold, the MIMO receiver may enlarge the RE group withrespect to the next RB. That is, if the channel correlation issufficiently good, the iteration number of the numerical analysisalgorithm does not significantly increase even when the RE group isenlarged. Therefore, the MIMO receiver may enlarge the RE group in orderto reduce the computational complexity of the reference RE.

Further, if the maximum iteration number of the numerical analysisalgorithm for the previous RB is less than the specific threshold, theMIMO receiver compares convergence speed in the frequency-axis directionwith the convergence speed in the time-axis direction to determine theconfiguration of the RE group to be enlarged. In addition, the MIMOreceiver may compare channel correlations on the frequency axis and thetime axis to determine the configuration of the RE group to be enlarged.The embodiment in which the RE group is enlarged is similarly applicableto the embodiment in which the RE group is reduced and thus a detaileddescription thereof will be omitted.

If the RE group is enlarged in consideration of the iteration number ofthe previous RB, the MIMO receiver may enlarge the RE group to bereturned to the unreduced RE group. That is, if the MIMO receiver hasreduced the RE group in consideration of the iteration number for theRB, enlarging the RE group may mean that the RE group is returned to theunreduced RE group.

FIG. 21 is a diagram showing a process of forming RE groups according toan embodiment of the present invention. In FIG. 21, each rectangleindicates an RB and each RB includes a plurality of REs shown in FIGS.16 to 20 and is composed of one or more RE groups. One or more of theembodiments described with reference to FIGS. 16 to 20 are combined andapplicable and thus the MIMO receiver can minimize the computationalcomplexity of each RB.

For example, first, the MIMO receiver determines the configuration ofthe RE group in consideration of the channel correlation according tothe frequency axis and the time axis with respect to RB #1 2111. Whendata detection of RB #1 2111 is finished, the MIMO receiver maypredetermine the configuration of the RB group to be used in RB #2 2112based on the convergence speed and the iteration number of the numericalanalysis algorithm for RB #1 2111. Similarly, the configuration of theRE group of RB #3 2113 may be determined based on the result of thenumerical analysis algorithm for RB #2 2112. That is, the configurationof the RE group to be used in the next RB may be determined based on theconvergence speed and the iteration number of the numerical analysisalgorithm for the previous RB and the RE group of a first RB may bedetermined in consideration of channel correlation between REs and/orSNR/SIR/SINR. The MIMO receiver may form RE groups in subframe ortimeslot units in addition to RB units.

As described above, the MIMO may adaptively determine the configurationof the RE group in consideration of channel correlation between REs, anSNR/SIR/SINR and previous operation history. Since the RE group isadaptively determined, the MIMO receiver can reduce computationalcomplexity required to process all RBs without performancedeterioration.

4. Apparatus Configuration

FIG. 22 is a block diagram showing the configuration of a UE and a basestation according to one embodiment of the present invention.

In FIG. 22, a UE 100 and the base station 200 may include radiofrequency (RF) units 110 and 210, processors 120 and 220 and memories130 and 230, respectively. Although a 1:1 communication environmentbetween the UE 100 and the base station 200 is shown in FIG. 19, acommunication environment may be established between a plurality of UEsand the base station 200. In addition, the base station 200 shown inFIG. 22 is applicable to a macro cell base station and a small cell basestation.

The RF units 110 and 210 may include transmitters 112 and 212 andreceivers 114 and 214, respectively. The transmitter 112 and thereceiver 114 of the UE 100 are configured to transmit and receivesignals to and from the base station 200 and other UEs and the processor120 is functionally connected to the transmitter 112 and the receiver114 to control a process of, at the transmitter 112 and the receiver114, transmitting and receiving signals to and from other apparatuses.The processor 120 processes a signal to be transmitted, sends theprocessed signal to the transmitter 112 and processes a signal receivedby the receiver 114.

If necessary, the processor 120 may store information included in anexchanged message in the memory 130. By this structure, the UE 100 mayperform the methods of the various embodiments of the present invention.

The transmitter 212 and the receiver 214 of the base station 200 areconfigured to transmit and receive signals to and from another basestation and the UEs and the processor 220 are functionally connected tothe transmitter 212 and the receiver 214 to control a process of, at thetransmitter 212 and the receiver 214, transmitting and receiving signalsto and from other apparatuses. The processor 220 processes a signal tobe transmitted, sends the processed signal to the transmitter 212 andprocesses a signal received by the receiver 214. If necessary, theprocessor 220 may store information included in an exchanged message inthe memory 230. By this structure, the base station 200 may perform themethods of the various embodiments of the present invention.

The processors 120 and 220 of the UE 100 and the base station 200instruct (for example, control, adjust, or manage) the operations of theUE 100 and the base station 200, respectively. The processors 120 and220 may be connected to the memories 130 and 180 for storing programcode and data, respectively. The memories 130 and 180 are respectivelyconnected to the processors 120 and 220 so as to store operatingsystems, applications and general files.

The processors 120 and 220 of the present invention may be calledcontrollers, microcontrollers, microprocessors, microcomputers, etc. Theprocessors 120 and 220 may be implemented by hardware, firmware,software, or a combination thereof. If the embodiments of the presentinvention are implemented by hardware, Application Specific IntegratedCircuits (ASICs), Digital Signal Processors (DSPs), Digital SignalProcessing Devices (DSPDs), Programmable Logic Devices (PLDs), FieldProgrammable Gate Arrays (FPGAs), etc. may be included in the processors120 and 220.

The present invention can also be embodied as computer-readable code ona computer-readable recording medium. The computer-readable recordingmedium includes all data storage devices that can store data which canbe thereafter read by a computer system. Examples of thecomputer-readable recording medium include read-only memory (ROM),random-access memory (RAM), CD-ROMs, magnetic tapes, floppy disks,optical data storage devices, and carrier waves (such as datatransmission through the Internet). The computer-readable recordingmedium can also be distributed over network coupled computer systems sothat the computer readable code is stored and executed in a distributedfashion.

It will be apparent to those skilled in the art that variousmodifications and variations can be made in the present inventionwithout departing from the spirit or scope of the inventions. Thus, itis intended that the present invention covers the modifications andvariations of this invention provided they come within the scope of theappended claims and their equivalents.

What is claimed is:
 1. A method of processing for decoding a signalreceived at a multiple input multiple output (MIMO) receiver including aplurality of antennas, the method comprising: receiving, at a receiverof the MIMO receiver, the signal including a plurality of resourceelements (REs); forming, at a processor of the MIMO receiver, an REgroup from among the plurality of REs in consideration of channelcorrelation between the plurality of REs; selecting, at the processor, areference RE for the RE group; generating, at the processor, apreprocessing filter to be shared for REs of the RE group based onchannel information of the reference RE; and generating, at theprocessor, detection signals for the REs of the RE group based on thepreprocessing filter, wherein each of the detection signals is a signaldetected from a corresponding RE using the preprocessing filter, whereinselecting the reference RE comprises: selecting, as the reference RE, anRE located at a position where a maximum distance from another RE amongthe REs of the RE group is minimized, wherein the maximum distance isexpressed by a distance on a frequency axis and a distance on a timeaxis.
 2. The method according to claim 1, wherein the REs of the REgroup are arranged along the frequency axis and the time axis and aconfiguration of the RE group is determined based on a number and ashape of the arranged REs.
 3. The method according to claim 1, whereinforming the RE group comprises: forming one or more RE groups bydividing a mother group composed of a resource block (RB), a subframe ora slot.
 4. The method according to claim 1, wherein forming the RE groupcomprises: comparing the channel correlation between the plurality ofREs computed along the frequency axis with a first threshold; andcomparing the channel correlation between the plurality of REs computedalong the time axis with a second threshold.
 5. The method according toclaim 4, wherein forming the RE group further comprises: selecting an REbefore the channel correlation computed along the frequency axis becomesless than the first threshold as a border on the frequency axis of theRE group; and selecting an RE before the channel correlation computedalong the time axis becomes less than the second threshold as a borderon the time axis of the RE group.
 6. The method according to claim 1,wherein forming the RE group comprises: determining an error allowablecoefficient of a numerical analysis algorithm to be used in a process ofgenerating the detection signal based on at least one of a signal tonoise ratio (SNR), signal to interference ratio (SIR) or signal tointerference plus noise ratio (SINR) of the received signal of each ofthe plurality of REs.
 7. The method according to claim 6, whereinforming the RE group further comprises: determining a threshold to beused in a process of computing the channel correlation based on at leastone of the SNR, SIR or SINR of the received signal of each of theplurality of REs.
 8. The method according to claim 1, furthercomprising: controlling a configuration of the RE group based on aconvergence speed of a numerical analysis algorithm performed in aprocess of generating the detection signals.
 9. The method according toclaim 8, wherein controlling the configuration comprises: comparingincrement of an iteration number in a frequency-axis direction withincrement of an iteration number in a time-axis direction and reducingthe size of the RE group in an axis direction in which the iterationnumber is more rapidly increased.
 10. A multiple input multiple output(MIMO) receiver including a plurality of antennas and configured toprocess decoding a signal received through the plurality of antennas,the MIMO receiver comprising: a transmitter; a receiver; and a processorconnected to the transmitter and the receiver, wherein the processorcontrols the receiver to receive the signal including a plurality ofresource elements (REs), forms an RE group from among the plurality ofREs in consideration of channel correlation between the plurality ofREs, selects a reference RE for the RE group, generates a preprocessingfilter to be shared for REs of the RE group based on channel informationof the reference RE, and generates detection signals for the REs of theRE group based on the preprocessing filter, wherein each of thedetection signals is a signal detected from a corresponding RE using thepreprocessing filter, wherein the processor selects, as the referenceRE, an RE located at a position where a maximum distance from another REamong the REs of the RE group is minimized, and wherein the maximumdistance is expressed by a distance on a frequency axis and a distanceon a time axis.
 11. The MIMO receiver according to claim 10, wherein theREs of the RE group are arranged along the frequency axis and the timeaxis and a configuration the RE group is determined based on a numberand a shape of the arranged REs.
 12. The MIMO receiver according toclaim 10, wherein the processor controls the configuration of the REgroup based on a convergence speed of a numerical analysis algorithmperformed in a process of generating the detection signals.
 13. The MIMOreceiver according to claim 12, wherein the processor compares incrementof an iteration number in a frequency-axis direction with increment ofan iteration number in a time-axis direction and reduces the size of theRE group in an axis direction in which the iteration number is morerapidly increased.
 14. The MIMO receiver according to claim 10, whereinthe processor compares the channel correlation between the plurality ofREs computed along the frequency axis with a first threshold andcompares the channel correlation between the plurality of REs computedalong the time axis with a second threshold to form the RE group. 15.The MIMO receiver according to claim 14, wherein the processor selectsan RE before the channel correlation computed along the frequency axisbecomes less than the first threshold as a border on the frequency axisof the RE group and selects an RE before the channel correlationcomputed along the time axis becomes less than the second threshold as aborder on the time axis of the RE group.
 16. The MIMO receiver accordingto claim 10, wherein the processor determines an error allowablecoefficient of a numerical analysis algorithm to be used in a process ofgenerating the detection signal based on at least one of a signal tonoise ratio (SNR), signal to interference ratio (SIR) or signal tointerference plus noise ratio (SINR) of the received signal of each ofthe plurality of REs.
 17. The MIMO receiver according to claim 16,wherein the processor determines a threshold to be used in a process ofcomputing the channel correlation based on at least one of the SNR, SIRor SINR of the received signal of each of the plurality of REs.
 18. TheMIMO receiver according to claim 10, wherein the processor forms one ormore RE groups by dividing a mother group composed of a resource block(RB), a subframe or a slot.